Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations20640
Missing cells207
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

households is highly overall correlated with population and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
population is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_rooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms has 207 (1.0%) missing valuesMissing

Reproduction

Analysis started2024-09-11 14:29:18.403214
Analysis finished2024-09-11 14:29:22.928251
Duration4.53 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct844
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.5697
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative20640
Negative (%)100.0%
Memory size161.4 KiB
2024-09-11T20:29:22.978253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.49
Q3-118.01
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0035317
Coefficient of variation (CV)-0.016756182
Kurtosis-1.3301524
Mean-119.5697
Median Absolute Deviation (MAD)1.28
Skewness-0.29780121
Sum-2467918.7
Variance4.0141394
MonotonicityNot monotonic
2024-09-11T20:29:23.114269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31 162
 
0.8%
-118.3 160
 
0.8%
-118.29 148
 
0.7%
-118.27 144
 
0.7%
-118.32 142
 
0.7%
-118.28 141
 
0.7%
-118.35 140
 
0.7%
-118.36 138
 
0.7%
-118.19 135
 
0.7%
-118.37 128
 
0.6%
Other values (834) 19202
93.0%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
< 0.1%
-124.18 6
< 0.1%
ValueCountFrequency (%)
-114.31 1
 
< 0.1%
-114.47 1
 
< 0.1%
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.56 1
 
< 0.1%
-114.57 3
< 0.1%
-114.58 2
< 0.1%
-114.59 2
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct862
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.631861
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.180272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.93
median34.26
Q337.71
95-th percentile38.96
Maximum41.95
Range9.41
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation2.1359524
Coefficient of variation (CV)0.059945013
Kurtosis-1.1177598
Mean35.631861
Median Absolute Deviation (MAD)1.23
Skewness0.465953
Sum735441.62
Variance4.5622926
MonotonicityNot monotonic
2024-09-11T20:29:23.246777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06 244
 
1.2%
34.05 236
 
1.1%
34.08 234
 
1.1%
34.07 231
 
1.1%
34.04 221
 
1.1%
34.09 212
 
1.0%
34.02 208
 
1.0%
34.1 203
 
1.0%
34.03 193
 
0.9%
33.93 181
 
0.9%
Other values (852) 18477
89.5%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 3
 
< 0.1%
32.56 10
 
< 0.1%
32.57 18
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 9
 
< 0.1%
32.61 14
0.1%
32.62 13
0.1%
32.63 18
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.82 1
 
< 0.1%
41.81 2
< 0.1%
41.8 3
< 0.1%
41.79 1
 
< 0.1%
41.78 3
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.639486
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.313289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.585558
Coefficient of variation (CV)0.43944774
Kurtosis-0.80062885
Mean28.639486
Median Absolute Deviation (MAD)10
Skewness0.060330638
Sum591119
Variance158.39626
MonotonicityNot monotonic
2024-09-11T20:29:23.382293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1273
 
6.2%
36 862
 
4.2%
35 824
 
4.0%
16 771
 
3.7%
17 698
 
3.4%
34 689
 
3.3%
26 619
 
3.0%
33 615
 
3.0%
18 570
 
2.8%
25 566
 
2.7%
Other values (42) 13153
63.7%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 58
 
0.3%
3 62
 
0.3%
4 191
0.9%
5 244
1.2%
6 160
0.8%
7 175
0.8%
8 206
1.0%
9 205
1.0%
10 264
1.3%
ValueCountFrequency (%)
52 1273
6.2%
51 48
 
0.2%
50 136
 
0.7%
49 134
 
0.6%
48 177
 
0.9%
47 198
 
1.0%
46 245
 
1.2%
45 294
 
1.4%
44 356
 
1.7%
43 353
 
1.7%

total_rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct5926
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2635.7631
Minimum2
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.453806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile620.95
Q11447.75
median2127
Q33148
95-th percentile6213.2
Maximum39320
Range39318
Interquartile range (IQR)1700.25

Descriptive statistics

Standard deviation2181.6153
Coefficient of variation (CV)0.82769778
Kurtosis32.630927
Mean2635.7631
Median Absolute Deviation (MAD)797
Skewness4.1473435
Sum54402150
Variance4759445.1
MonotonicityNot monotonic
2024-09-11T20:29:23.527324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 18
 
0.1%
1613 17
 
0.1%
1582 17
 
0.1%
2127 16
 
0.1%
1717 15
 
0.1%
2053 15
 
0.1%
1607 15
 
0.1%
1722 15
 
0.1%
1471 15
 
0.1%
1703 15
 
0.1%
Other values (5916) 20482
99.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 4
< 0.1%
19 2
< 0.1%
20 2
< 0.1%
ValueCountFrequency (%)
39320 1
< 0.1%
37937 1
< 0.1%
32627 1
< 0.1%
32054 1
< 0.1%
30450 1
< 0.1%
30405 1
< 0.1%
30401 1
< 0.1%
28258 1
< 0.1%
27870 1
< 0.1%
27700 1
< 0.1%

total_bedrooms
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1923
Distinct (%)9.4%
Missing207
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean537.87055
Minimum1
Maximum6445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.601846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137
Q1296
median435
Q3647
95-th percentile1275.4
Maximum6445
Range6444
Interquartile range (IQR)351

Descriptive statistics

Standard deviation421.38507
Coefficient of variation (CV)0.78343213
Kurtosis21.985575
Mean537.87055
Median Absolute Deviation (MAD)162
Skewness3.4595463
Sum10990309
Variance177565.38
MonotonicityNot monotonic
2024-09-11T20:29:23.673088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 55
 
0.3%
331 51
 
0.2%
345 50
 
0.2%
343 49
 
0.2%
393 49
 
0.2%
348 48
 
0.2%
394 48
 
0.2%
328 48
 
0.2%
309 47
 
0.2%
272 47
 
0.2%
Other values (1913) 19941
96.6%
(Missing) 207
 
1.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
< 0.1%
4 7
< 0.1%
5 6
< 0.1%
6 5
< 0.1%
7 6
< 0.1%
8 8
< 0.1%
9 7
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
6445 1
< 0.1%
6210 1
< 0.1%
5471 1
< 0.1%
5419 1
< 0.1%
5290 1
< 0.1%
5033 1
< 0.1%
5027 1
< 0.1%
4957 1
< 0.1%
4952 1
< 0.1%
4819 1
< 0.1%

population
Real number (ℝ)

HIGH CORRELATION 

Distinct3888
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.4767
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.738651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile348
Q1787
median1166
Q31725
95-th percentile3288
Maximum35682
Range35679
Interquartile range (IQR)938

Descriptive statistics

Standard deviation1132.4621
Coefficient of variation (CV)0.79444447
Kurtosis73.553116
Mean1425.4767
Median Absolute Deviation (MAD)440
Skewness4.9358582
Sum29421840
Variance1282470.5
MonotonicityNot monotonic
2024-09-11T20:29:23.807668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 25
 
0.1%
761 24
 
0.1%
1227 24
 
0.1%
1052 24
 
0.1%
850 24
 
0.1%
825 23
 
0.1%
782 22
 
0.1%
999 22
 
0.1%
1005 22
 
0.1%
753 21
 
0.1%
Other values (3878) 20409
98.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 4
< 0.1%
9 2
< 0.1%
11 1
 
< 0.1%
13 4
< 0.1%
14 3
< 0.1%
15 2
< 0.1%
17 2
< 0.1%
ValueCountFrequency (%)
35682 1
< 0.1%
28566 1
< 0.1%
16305 1
< 0.1%
16122 1
< 0.1%
15507 1
< 0.1%
15037 1
< 0.1%
13251 1
< 0.1%
12873 1
< 0.1%
12427 1
< 0.1%
12203 1
< 0.1%

households
Real number (ℝ)

HIGH CORRELATION 

Distinct1815
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.53968
Minimum1
Maximum6082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:23.875998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q1280
median409
Q3605
95-th percentile1162
Maximum6082
Range6081
Interquartile range (IQR)325

Descriptive statistics

Standard deviation382.32975
Coefficient of variation (CV)0.76536413
Kurtosis22.057988
Mean499.53968
Median Absolute Deviation (MAD)151
Skewness3.4104377
Sum10310499
Variance146176.04
MonotonicityNot monotonic
2024-09-11T20:29:23.942390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 57
 
0.3%
386 56
 
0.3%
335 56
 
0.3%
282 55
 
0.3%
429 54
 
0.3%
375 53
 
0.3%
284 51
 
0.2%
297 51
 
0.2%
278 50
 
0.2%
340 50
 
0.2%
Other values (1805) 20107
97.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 7
< 0.1%
6 5
< 0.1%
7 10
< 0.1%
8 8
< 0.1%
9 9
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
6082 1
< 0.1%
5358 1
< 0.1%
5189 1
< 0.1%
5050 1
< 0.1%
4930 1
< 0.1%
4855 1
< 0.1%
4769 1
< 0.1%
4616 1
< 0.1%
4490 1
< 0.1%
4372 1
< 0.1%

median_income
Real number (ℝ)

HIGH CORRELATION 

Distinct12928
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.870671
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:24.008198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60057
Q12.5634
median3.5348
Q34.74325
95-th percentile7.300305
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.17985

Descriptive statistics

Standard deviation1.8998217
Coefficient of variation (CV)0.4908249
Kurtosis4.9525241
Mean3.870671
Median Absolute Deviation (MAD)1.0642
Skewness1.6466567
Sum79890.65
Variance3.6093226
MonotonicityNot monotonic
2024-09-11T20:29:24.079438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 49
 
0.2%
15.0001 49
 
0.2%
2.875 46
 
0.2%
2.625 44
 
0.2%
4.125 44
 
0.2%
3.875 41
 
0.2%
3.375 38
 
0.2%
3 38
 
0.2%
4 37
 
0.2%
3.625 37
 
0.2%
Other values (12918) 20217
98.0%
ValueCountFrequency (%)
0.4999 12
0.1%
0.536 10
< 0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.696 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 49
0.2%
15 2
 
< 0.1%
14.9009 1
 
< 0.1%
14.5833 1
 
< 0.1%
14.4219 1
 
< 0.1%
14.4113 1
 
< 0.1%
14.2959 1
 
< 0.1%
14.2867 1
 
< 0.1%
13.947 1
 
< 0.1%
13.8556 1
 
< 0.1%

median_house_value
Real number (ℝ)

HIGH CORRELATION 

Distinct3842
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206855.82
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2024-09-11T20:29:24.149652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile66200
Q1119600
median179700
Q3264725
95-th percentile489810
Maximum500001
Range485002
Interquartile range (IQR)145125

Descriptive statistics

Standard deviation115395.62
Coefficient of variation (CV)0.55785531
Kurtosis0.32787024
Mean206855.82
Median Absolute Deviation (MAD)68400
Skewness0.97776327
Sum4.2695041 × 109
Variance1.3316148 × 1010
MonotonicityNot monotonic
2024-09-11T20:29:24.219646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 965
 
4.7%
137500 122
 
0.6%
162500 117
 
0.6%
112500 103
 
0.5%
187500 93
 
0.5%
225000 92
 
0.4%
350000 79
 
0.4%
87500 78
 
0.4%
275000 65
 
0.3%
150000 64
 
0.3%
Other values (3832) 18862
91.4%
ValueCountFrequency (%)
14999 4
< 0.1%
17500 1
 
< 0.1%
22500 4
< 0.1%
25000 1
 
< 0.1%
26600 1
 
< 0.1%
26900 1
 
< 0.1%
27500 1
 
< 0.1%
28300 1
 
< 0.1%
30000 2
< 0.1%
32500 4
< 0.1%
ValueCountFrequency (%)
500001 965
4.7%
500000 27
 
0.1%
499100 1
 
< 0.1%
499000 1
 
< 0.1%
498800 1
 
< 0.1%
498700 1
 
< 0.1%
498600 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.4 KiB
<1H OCEAN
9136 
INLAND
6551 
NEAR OCEAN
2658 
NEAR BAY
2290 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0649225
Min length6

Characters and Unicode

Total characters166460
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN 9136
44.3%
INLAND 6551
31.7%
NEAR OCEAN 2658
 
12.9%
NEAR BAY 2290
 
11.1%
ISLAND 5
 
< 0.1%

Length

2024-09-11T20:29:24.288556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T20:29:24.354213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ocean 11794
34.0%
1h 9136
26.3%
inland 6551
18.9%
near 4948
14.2%
bay 2290
 
6.6%
island 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Interactions

2024-09-11T20:29:22.318709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.624727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.113286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.536333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.979384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.446941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.894499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.412058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.859649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.367710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.674728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.159287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.583842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.030893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.495453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.944500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.460058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.910162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.414218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.720238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.201799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.631850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.079896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.542454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.052012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.506568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.959158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.463219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.768239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.247797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.681850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.130404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.590967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.101522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.554568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.006666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.517727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.866750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.299309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.733356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.184920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.643965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.154521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.607085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.062666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.567727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.915263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.346309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.783866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.236919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.692477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.207034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.656085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.112691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.621237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:18.966264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.394821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.833871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.289429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.745478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.258034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.708559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.166691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.671238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.015776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.442822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.881873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.341430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.793989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.308545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.757558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.217200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.722743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.064776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.489333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:19.932381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.394941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:20.845989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.360545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:21.808650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T20:29:22.269204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-11T20:29:24.402774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
householdshousing_median_agelatitudelongitudemedian_house_valuemedian_incomeocean_proximitypopulationtotal_bedroomstotal_rooms
households1.000-0.282-0.0740.0600.1130.0300.0190.9040.9760.907
housing_median_age-0.2821.0000.032-0.1510.075-0.1470.190-0.284-0.307-0.357
latitude-0.0740.0321.000-0.879-0.166-0.0880.470-0.124-0.057-0.018
longitude0.060-0.151-0.8791.000-0.070-0.0100.4250.1240.0640.040
median_house_value0.1130.075-0.166-0.0701.0000.6770.3020.0040.0860.206
median_income0.030-0.147-0.088-0.0100.6771.0000.1250.006-0.0060.271
ocean_proximity0.0190.1900.4700.4250.3020.1251.0000.0140.0170.021
population0.904-0.284-0.1240.1240.0040.0060.0141.0000.8710.816
total_bedrooms0.976-0.307-0.0570.0640.086-0.0060.0170.8711.0000.915
total_rooms0.907-0.357-0.0180.0400.2060.2710.0210.8160.9151.000

Missing values

2024-09-11T20:29:22.789252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-11T20:29:22.876255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
5-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
6-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
7-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY
8-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY
9-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
20630-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND
20631-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND
20632-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND
20633-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND
20634-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND